With the increase of large inter-linked open data sets made available on the web, there is a growing interest in tools that allow to quickly and easily store, transform, query, mine and visualize that data.
CubicWeb is a semantic web framework written in Python that has been succesfully used in large-scale projects, such as http://data.bnf.fr (French National Library’s opendata) or Collections des musées de Haute-Normandie (museums of Haute-Normandie). Using a browser connected to the server via HTTP, the user can enter queries in a high-level query language, similar to SPARQL but called RQL, that operates over a relational database (PostgreSQL in our case).
Using Protovis, views will include maps, charts, hierarchies, networks, statistics, etc. A important feature is that any tuple (query, processor, view) has a corresponding url, making all results addressable, linkable and shareable.
More technical details can be found in this blog post : "Data Fast-food": quick interactive exploratory processing and visualization of complex datasets with CubicWeb.